Electromyography (EMG)

9 papers with code • 0 benchmarks • 1 datasets

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Greatest papers with code

An Improved Model for Voicing Silent Speech

dgaddy/silent_speech 3 Jun 2021

In this paper, we present an improved model for voicing silent speech, where audio is synthesized from facial electromyography (EMG) signals.

Electromyography (EMG)

Digital Voicing of Silent Speech

dgaddy/silent_speech EMNLP 2020

In this paper, we consider the task of digitally voicing silent speech, where silently mouthed words are converted to audible speech based on electromyography (EMG) sensor measurements that capture muscle impulses.

Electromyography (EMG) Speech Synthesis

Parkinson’s Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer

larocs/EMG-GAN 3 May 2020

This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson’s Disease (PD) electromyography (EMG) signals.

Data Augmentation Electromyography (EMG) +1

EV-Action: Electromyography-Vision Multi-Modal Action Dataset

wanglichenxj/EV-Action-Electromyography-Vision-Multi-Modal-Action-Dataset 20 Apr 2019

To make up this, we introduce a new, large-scale EV-Action dataset in this work, which consists of RGB, depth, electromyography (EMG), and two skeleton modalities.

Action Recognition Electromyography (EMG) +1

sEMG Gesture Recognition with a Simple Model of Attention

josephsdavid/attentionsemg 5 Jun 2020

Myoelectric control is one of the leading areas of research in the field of robotic prosthetics.

Electromyography (EMG) Gesture Recognition +1

End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning

sharif1093/dextron 26 Apr 2021

State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues.

Electromyography (EMG) Imitation Learning

Parkinson’s Disease EMG Signal Prediction Using Neural Networks

larocs/EMG-prediction 6 Oct 2019

This paper proposes a comparison between different neural network models, using multilayer perceptron (MLPs) and recurrent neural network (RNN) models, for predicting Parkinson's disease electromyography (EMG) signals, to anticipate resulting resting tremor patterns.

EMG Signal Prediction

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

coreylammie/TBCAS-Towards-Healthcare-and-Biomedical-Applications 11 Jul 2020

The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge.

Electromyography (EMG) Sensor Fusion